import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import rcParams
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC ##importing from svm library Support Vector Classifier
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV

rcParams['figure.figsize'] = 15, 5
sns.set_style('darkgrid')

bcdata= pd.read_csv('data.csv')
bcdata.head()
bcdata.tail()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	NaN
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	NaN
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	NaN
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	NaN
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	NaN

5 rows × 33 columns

bcdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB

bcdata.drop(labels=['Unnamed: 32', 'id'], axis=1, inplace=True)

col_names = list(bcdata.columns)
mean_names = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']
se_names = ['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']
worst_names = ['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']
print(col_names) #testing

['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']

doing pairplots below just so i can see the mean, se and worst of each attribute's visuals.

sns.pairplot(bcdata, hue='diagnosis', vars=mean_names)
plt.show()

sns.pairplot(bcdata, hue='diagnosis', vars=se_names)
plt.show()

sns.pairplot(bcdata, hue='diagnosis', vars=worst_names)
plt.show()

##this is staying the same because were lookng for balance in diagnosis

sns.countplot(x=bcdata['diagnosis'])
plt.show()

sns.scatterplot(x = 'area_mean', y = 'smoothness_mean', hue = 'diagnosis', data = bcdata)
plt.show()

plt.figure(figsize=(30,20))
sns.heatmap(bcdata.corr(), annot=True, center = 4)
plt.show()

bcdata_copy = bcdata.copy()

bcdata_copy = bcdata_copy.drop(columns = ['diagnosis','perimeter_mean', 'radius_mean','texture_se', 'perimeter_se', 'symmetry_se'])

plt.figure(figsize=(30,20))
sns.heatmap(bcdata_copy.corr(), annot=True, center = 4)
plt.show()

Y = bcdata['diagnosis']
X = bcdata.drop('diagnosis', axis=1)

scaler = MinMaxScaler()
X_sc = scaler.fit_transform(X) ##turns this into a numpy, so we have to use pd.dataframe to explicitly convert it into pd df 

X = pd.DataFrame(data=X_sc, columns= X.columns)
X.tail()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
564	0.690000	0.428813	0.678668	0.566490	0.526948	0.296055	0.571462	0.690358	0.336364	0.132056	...	0.623266	0.383262	0.576174	0.452664	0.461137	0.178527	0.328035	0.761512	0.097575	0.105667
565	0.622320	0.626987	0.604036	0.474019	0.407782	0.257714	0.337395	0.486630	0.349495	0.113100	...	0.560655	0.699094	0.520892	0.379915	0.300007	0.159997	0.256789	0.559450	0.198502	0.074315
566	0.455251	0.621238	0.445788	0.303118	0.288165	0.254340	0.216753	0.263519	0.267677	0.137321	...	0.393099	0.589019	0.379949	0.230731	0.282177	0.273705	0.271805	0.487285	0.128721	0.151909
567	0.644564	0.663510	0.665538	0.475716	0.588336	0.790197	0.823336	0.755467	0.675253	0.425442	...	0.633582	0.730277	0.668310	0.402035	0.619626	0.815758	0.749760	0.910653	0.497142	0.452315
568	0.036869	0.501522	0.028540	0.015907	0.000000	0.074351	0.000000	0.000000	0.266162	0.187026	...	0.054287	0.489072	0.043578	0.020497	0.124084	0.036043	0.000000	0.000000	0.257441	0.100682

5 rows × 30 columns

##create model

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=.3, random_state=98)

svm_m = SVC()
svm_m.fit(X_train, Y_train)

SVC()

preds = svm_m.predict(X_test)
print(classification_report(Y_test, preds))

              precision    recall  f1-score   support

           B       0.98      0.98      0.98       112
           M       0.97      0.97      0.97        59

    accuracy                           0.98       171
   macro avg       0.97      0.97      0.97       171
weighted avg       0.98      0.98      0.98       171

parameter_grid = {'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']}
##model, dict of parameters want to try
grid = GridSearchCV(SVC(), parameter_grid, verbose=5) ##theres another one that tests subsets, grid search cv

grid.fit(X_train,Y_train)

Fitting 5 folds for each of 25 candidates, totalling 125 fits
[CV 1/5] END .....................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV 2/5] END .....................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
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[CV 5/5] END .....................C=0.1, gamma=1, kernel=rbf; total time=   0.0s
[CV 1/5] END ...................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 2/5] END ...................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 3/5] END ...................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 4/5] END ...................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 5/5] END ...................C=0.1, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 1/5] END ..................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
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[CV 5/5] END ..................C=0.1, gamma=0.01, kernel=rbf; total time=   0.0s
[CV 1/5] END .................C=0.1, gamma=0.001, kernel=rbf; total time=   0.0s
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[CV 1/5] END ......................C=10, gamma=1, kernel=rbf; total time=   0.0s
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[CV 1/5] END ...................C=10, gamma=0.01, kernel=rbf; total time=   0.0s
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[CV 1/5] END .................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
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[CV 5/5] END .................C=10, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 1/5] END .....................C=100, gamma=1, kernel=rbf; total time=   0.0s
[CV 2/5] END .....................C=100, gamma=1, kernel=rbf; total time=   0.0s
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[CV 1/5] END ...................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 2/5] END ...................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
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[CV 5/5] END ...................C=100, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 1/5] END ..................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
[CV 2/5] END ..................C=100, gamma=0.01, kernel=rbf; total time=   0.0s
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[CV 1/5] END .................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV 2/5] END .................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
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[CV 5/5] END .................C=100, gamma=0.001, kernel=rbf; total time=   0.0s
[CV 1/5] END ................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
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[CV 5/5] END ................C=100, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 1/5] END ....................C=1000, gamma=1, kernel=rbf; total time=   0.0s
[CV 2/5] END ....................C=1000, gamma=1, kernel=rbf; total time=   0.0s
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[CV 1/5] END ..................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
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[CV 5/5] END ..................C=1000, gamma=0.1, kernel=rbf; total time=   0.0s
[CV 1/5] END .................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV 2/5] END .................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
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[CV 5/5] END .................C=1000, gamma=0.01, kernel=rbf; total time=   0.0s
[CV 1/5] END ................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV 2/5] END ................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV 3/5] END ................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
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[CV 5/5] END ................C=1000, gamma=0.001, kernel=rbf; total time=   0.0s
[CV 1/5] END ...............C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 2/5] END ...............C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 3/5] END ...............C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 4/5] END ...............C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s
[CV 5/5] END ...............C=1000, gamma=0.0001, kernel=rbf; total time=   0.0s

GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100, 1000],
                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],
                         'kernel': ['rbf']},
             verbose=5)

##this outputs the best chosen parameter. 
grid.best_params_

{'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}

grid_predictions = grid.predict(X_test)

print(classification_report(Y_test, grid_predictions))

              precision    recall  f1-score   support

           B       0.97      0.96      0.96       112
           M       0.92      0.95      0.93        59

    accuracy                           0.95       171
   macro avg       0.95      0.95      0.95       171
weighted avg       0.95      0.95      0.95       171